Hiring data engineers for AI initiatives: skills to look for

Data engineers have quickly become indispensable to any AI initiative. As companies double down on data-driven strategies, the demand for skilled data engineers is surging. In fact, the World Economic Forum’s Future of Jobs Report 2025 identifies Data Engineers as one of the key roles poised for significant growth amid digital transformation. A recent Gartner report even forecasts a 90% increase in demand for data engineers by this year due to businesses’ growing reliance on data and AI​

For HR and IT managers at large companies, this means competition for talent is fierce – and knowing what to look for in candidates is more critical than ever.

But hiring the right data engineers (at all levels of seniority) can power your AI projects with reliable, well-governed data. This blogpost will walk through why data engineers are essential to AI initiatives and which technical and soft skills to prioritize.

The Key Role of Data Engineers in AI Projects

AI and machine learning may grab headlines, but data engineers are the unsung heroes who make those AI projects possible. They design and maintain the data pipelines that transform raw, chaotic data into clean, structured information that data scientists and AI systems can actually use​

“Data doesn’t organize itself… That’s where data engineers step in. They build pipelines, clean up the mess, and make data accessible to analysts, scientists, and AI systems. Without them, all that valuable information is just… noise.”​

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In other words, even the most advanced AI model will fail without a robust data pipeline delivering high-quality data.

Crucially, data engineers ensure data availability, quality, and timeliness for AI. They handle everything from integrating diverse data sources to implementing ETL processes and monitoring data flow in production. This behind-the-scenes work directly impacts an AI initiative’s success – poor data quality or broken pipelines can derail model training or insights.

Equally important is how data engineers work collaboratively in AI teams. They don’t operate in isolation; rather, they partner with data scientists, ML engineers, and business analysts to understand data needs and deliver solutions. This cross-functional collaboration means that a great data engineer understands not just databases and code, but also the requirements of machine learning models and the business context of the data.

In summary, data engineers form the data foundation of AI initiatives. Their work enables AI specialists to focus on modeling and analysis, confident that the right data, in the right format, at the right time is available. Given this central role, it’s no surprise companies across industries are investing heavily in data engineering talent to power their AI-driven transformations.

Data engineering roles from junior to senior

Not all data engineering roles are the same – a junior hire will have a very different scope and skillset compared to a senior or lead, Maxime Campe, talent recruiter at I4M says. As you plan to hire, it’s important to understand these levels so you can target the right candidates and set the right expectations.

Junior Data Engineers (Entry Level)

Junior data engineers are early-career professionals who are building their foundation in data engineering. They typically focus on implementing and maintaining pipelines under the guidance of more senior staff. For example, a junior data engineer might spend most of their time writing scripts to extract and load data, or building simple data pipelines for a specific project​

Maxime’s tip: When evaluating junior candidates, look less for lengthy experience and more for strong fundamentals and enthusiasm. Evidence of SQL skills (an essential for any data engineer), some exposure to cloud or big data tools, and an ability to pick up new technologies are key. Many junior data engineers may showcase academic projects, bootcamp experience, or internships instead of full-time work – that’s okay as long as they demonstrate the core skills and a growth mindset.

Mid-Level Data Engineers

Mid-level data engineers (often just titled “Data Engineer”) are those who have a few years of experience and can work more independently. They are capable of taking a business problem and figuring out how to solve it with data pipelines or infrastructure, with only high-level guidance​

A mid-level engineer should be comfortable designing moderately complex pipelines, optimizing data workflows, and troubleshooting issues with minimal supervision. They typically own data pipelines or data models for specific business areas and ensure these are running reliably. At this stage, best practices become important – mid-level engineers should understand how to write efficient, maintainable data pipelines and be aware of things like version control, testing, and scheduling in data workflows. They also know when to seek help and how to collaborate with others if they hit a roadblock​

When hiring at this level, look for candidates who can point to concrete accomplishments – e.g. “designed a streaming data ingestion process that reduced pipeline latency by X%” or “implemented a data warehouse schema for department Y”. They should be able to discuss not just what tools they used, but why they designed the solution that way, indicating an ability to make architectural decisions.

Senior Data Engineers

Senior data engineers are experienced professionals who often serve as technical leaders on data teams. A senior data engineer designs complex data architectures, leads large-scale pipeline development, and mentors junior teammates. They have a deep understanding of the business domains they support and can translate business requirements into robust data solutions​

For instance, a senior might architect the data platform for a new AI product, choosing the right database, storage, and streaming technologies, and devising how data will flow end-to-end. They are also adept at performance tuning and can identify optimization opportunities across existing pipelines​

They often coordinate work across multiple engineers, set standards for code quality and data governance, and ensure the data infrastructure scales with growing needs. In many organizations, senior data engineers also collaborate closely with data architects or take on architect responsibilities themselves.

Lead/Principal Data Engineers

At the top of the individual-contributor ladder are lead or principal data engineers. (Titles vary – some companies use “Staff Data Engineer” or integrate this level into “Data Architect.”) These veterans have a broad, strategic view. They might oversee the data engineering strategy for an entire department or business unit​

A principal data engineer will be trusted to orchestrate enterprise-scale data architecture, making high-level decisions about data platforms, governance, and emerging technologies. They often set the technical roadmap for data engineering – for example, deciding if the company should adopt a new streaming platform or how to integrate a data lake with a data warehouse. In this role, there is a strong emphasis on teaching and upskilling the team, ensuring that the practices and knowledge are disseminated across the engineering group​.

Lead/principal engineers still get hands-on, but typically on the most complex, high-impact projects. They might build or refine the trickiest parts of a pipeline and delegate routine tasks to others. At this level, soft skills become as crucial as technical skills. As one industry observer notes, as engineers rise in seniority, “responsibility and scope expands” and the need for soft skills also increases as you rise the ranks, even as technical skills remain vital​

Key takeaway: As we hire, we match the candidate’s experience to the role’s scope. A junior won’t be designing your entire data architecture, and a senior will want more than writing basic SQL scripts. Understanding these differences helps in crafting appropriate job descriptions and interview questions. Next, let’s look at what specific skills successful data engineers bring to the table.

Key technical skills to seek in Data Engineers

Whether junior or senior, there is a core set of technical skills that any data engineer involved in AI initiatives should possess in our opinion. Here are the most important areas we at In4Matic evaluate:

• Data Pipeline Architecture & ETL: The ability to design, build, and maintain data pipelines is the heart of data engineering. Look for experience with ETL (Extract, Transform, Load) or ELT processes and tools. A strong candidate can discuss how they have moved data from various sources (databases, APIs, files, etc.) into a data lake or warehouse, and ensured that the pipeline is reliable and efficient. They should understand data modeling and schema design to organize raw data into usable structures. Data engineers often “design and build data pipelines for extracting, transforming, and loading data,” and optimize these pipelines for performance at scale​
  
  In modern AI projects, pipelines may need to handle real-time data as well as batch data. Experience with streaming data architectures (using tools like Apache Kafka, AWS Kinesis, or Spark Streaming) is a big plus.
  
  
• Cloud Platforms and Big Data Tools: Cloud expertise is essentially a must-have in today’s data engineering. Most AI data pipelines live on cloud platforms like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform. Candidates should be comfortable with cloud data services (e.g. AWS S3, Redshift, Azure Data Lake, BigQuery) and understand cloud-based architecture. They’ll likely need to deploy and orchestrate pipelines in the cloud, use infrastructure-as-code, and manage resources for cost and performance. Knowledge of containerization and orchestration (Docker, Kubernetes) can also be useful for deploying data processing jobs.
  
  
• Programming (SQL, Python, and More): SQL is non-negotiable – it remains the cornerstone of data work​. Data engineers should be fluent in SQL for querying and manipulating relational databases or data warehouses. Beyond SQL, a general-purpose programming language is needed for pipeline development – Python is by far the most common in data engineering (thanks to its ecosystem of libraries for data manipulation, automation, and interacting with cloud/big-data tools). In addition, many data engineers are proficient in a language like Java or Scala, especially if working with Hadoop/Spark or enterprise systems. When reviewing resumes, we check for evidence of strong SQL skills and programming in Python/Java/Scala. As one hiring guide puts it, ensure candidates can demonstrate proficiency in essential programming languages like Python and SQL and ability to use them in practice​.
  
  
• MLOps and Machine Learning Integration: Since we’re discussing data engineers in the context of AI initiatives, a standout candidate will understand how their data pipelines feed into the machine learning lifecycle. MLOps (Machine Learning Operations) is an emerging area that overlaps with data engineering – it involves automating and optimizing the deployment of ML models, including the data processes around them.

Essentially, a data engineer on an AI project should know how to deliver data for model training and inference in a reproducible, efficient way. For example, they might need to build a feature store or integrate real-time data into an online model. An understanding of how data versioning, model versioning, and continuous integration apply to ML can greatly facilitate collaboration with the data science team.

• Data Warehousing and Database Management: Storing and organizing large volumes of data is another core aspect. Candidates should know how to work with different types of storage: relational databases (SQL Server, Postgres, etc.), NoSQL databases, distributed file systems, and modern data warehouses (Snowflake, BigQuery, Redshift).
  
  
• Data Quality, Security, and Governance: Technical excellence isn’t just about moving data fast – it’s also about moving it correctly and securely. A strong data engineer pays attention to data quality, implementing validation checks and monitoring to ensure the data feeding AI models is accurate and consistent. They should be aware of data governance practices: managing metadata, data lineage, and access control. With increasing regulations around data (GDPR, etc.), having someone who understands compliance requirements for data pipelines is valuable.
  
  

By focusing on these technical skill areas, you increase the likelihood of hiring a data engineer who can effectively support your AI projects. As a quick summary, the ideal candidate’s toolkit should cover data pipeline/ETL expertise, cloud platform know-how, strong SQL and coding abilities, experience with big data and streaming, and an appreciation for data quality and MLOps. Indeed, a recent analysis found that professionals with combined expertise in “AI, cloud computing, and big data” will be essential for driving innovation – reflecting how intertwined these skills are in modern data roles​.